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Untangling biological factors influencing trajectory inference from single cell data.

ABSTRACT: Advances in single-cell RNA sequencing over the past decade has shifted the discussion of cell identity toward the transcriptional state of the cell. While the incredible resolution provided by single-cell RNA sequencing has led to great advances in unraveling tissue heterogeneity and inferring cell differentiation dynamics, it raises the question of which sources of variation are important for determining cellular identity. Here we show that confounding biological sources of variation, most notably the cell cycle, can distort the inference of differentiation trajectories. We show that by factorizing single cell data into distinct sources of variation, we can select a relevant set of factors that constitute the core regulators for trajectory inference, while filtering out confounding sources of variation (e.g. cell cycle) which can perturb the inferred trajectory. Script are available publicly on https://github.com/mochar/cell_variation.

SUBMITTER: Charrout M

PROVIDER: S-EPMC7671373 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Untangling biological factors influencing trajectory inference from single cell data.

Charrout Mohammed M Reinders Marcel J T MJT Mahfouz Ahmed A

NAR genomics and bioinformatics 20200722 3

Advances in single-cell RNA sequencing over the past decade has shifted the discussion of cell identity toward the transcriptional state of the cell. While the incredible resolution provided by single-cell RNA sequencing has led to great advances in unraveling tissue heterogeneity and inferring cell differentiation dynamics, it raises the question of which sources of variation are important for determining cellular identity. Here we show that confounding biological sources of variation, most not ...[more]

PMID: 33575604

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